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A Model-Based Systems Engineering (MBSE) Journey to Developing a Concept of Operations
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A Model-Based System Engineering Approach to Modern System Architectures
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The Mars Ascent Vehicle & Model-Based Systems Engineering
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A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System
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NASA/JAXA MBSE Meeting: It All Started with Model-based Systems Engineering
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IFAR Overview of Scientific Assessment and Model Based Systems Engineering Efforts
Presentation material for panel at SAE AeroTech.
Towards Implementing Artificial Thinking Autonomy with Model-Based Systems Engineering
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Applying Model Based Systems Engineering to Reduce Nuclear Weapons Development Cycle Time.
Abstract not provided.
Applying Model Based Systems Engineering to Reduce Weapon System Development Cycle Time.
Abstract not provided.
Barriers to Adoption of Model Based Systems Engineering (MBSE).
Abstract not provided.
From Machine Learning to Machine Reasoning: A Model-Based System Engineering Perspective
Presentation for DICE conference
Modeling in the State Flow Environment to Support Launch Vehicle Verification Testing for Mission and Fault Management Algorithms in the NASA Space Launch System
Analysis methods and testing processes are essential activities in the engineering development and verification of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS). Central to mission success is reliable verification of the Mission and Fault Management (M&FM) algorithms for the SLS launch vehicle (LV) flight software. This is particularly difficult because M&FM algorithms integrate and operate LV subsystems, which consist of diverse forms of hardware and software themselves, with equally diverse integration from the engineering disciplines of LV subsystems. M&FM operation of SLS requires a changing mix of LV automation. During pre-launch the LV is primarily operated by the Kennedy Space Center (KSC) Ground Systems Development and Operations (GSDO) organization with some LV automation of time-critical functions, and much more autonomous LV operations during ascent that have crucial interactions with the Orion crew capsule, its astronauts, and with mission controllers at the Johnson Space Center. M&FM algorithms must perform all nominal mission commanding via the flight computer to control LV states from pre-launch through disposal and also address failure conditions by initiating autonomous or commanded aborts (crew capsule escape from the failing LV), redundancy management of failing subsystems and components, and safing actions to reduce or prevent threats to ground systems and crew. To address the criticality of the verification testing of these algorithms, the NASA M&FM team has utilized the State Flow environment6 (SFE) with its existing Vehicle Management End-to-End Testbed (VMET) platform which also hosts vendor-supplied physics-based LV subsystem models. The human-derived M&FM algorithms are designed and vetted in Integrated Development Teams composed of design and development disciplines such as Systems Engineering, Flight Software (FSW), Safety and Mission Assurance (S&MA) and major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GN&C), Thrust Vector Control (TVC), liquid engines, and the astronaut crew office. Since the algorithms are realized using model-based engineering (MBE) methods from a hybrid of the Unified Modeling Language (UML) and Systems Modeling Language (SysML), SFE methods are a natural fit to provide an in depth analysis of the interactive behavior of these algorithms with the SLS LV subsystem models. For this, the M&FM algorithms and the SLS LV subsystem models are modeled using constructs provided by Matlab which also enables modeling of the accompanying interfaces providing greater flexibility for integrated testing and analysis, which helps forecast expected behavior in forward VMET integrated testing activities. In VMET, the M&FM algorithms are prototyped and implemented using the same C++ programming language and similar state machine architectural concepts used by the FSW group. Due to the interactive complexity of the algorithms, VMET testing thus far has verified all the individual M&FM subsystem algorithms with select subsystem vendor models but is steadily progressing to assessing the interactive behavior of these algorithms with LV subsystems, as represented by subsystem models. The novel SFE applications has proven to be useful for quick look analysis into early integrated system behavior and assessment of the M&FM algorithms with the modeled LV subsystems. This early MBE analysis generates vital insight into the integrated system behaviors, algorithm sensitivities, design issues, and has aided in the debugging of the M&FM algorithms well before full testing can begin in more expensive, higher fidelity but more arduous environments such as VMET, FSW testing, and the Systems Integration Lab7 (SIL). SFE has exhibited both expected and unexpected behaviors in nominal and off nominal test cases prior to full VMET testing. In many findings, these behavioral characteristics were used to correct the M&FM algorithms, enable better test coverage, and develop more effective test cases for each of the LV subsystems. This has improved the fidelity of testing and planning for the next generation of M&FM algorithms as the SLS program evolves from non-crewed to crewed flight, impacting subsystem configurations and the M&FM algorithms that control them. SFE analysis has improved robustness and reliability of the M&FM algorithms by revealing implementation errors and documentation inconsistencies. It is also improving planning efficiency for future VMET testing of the M&FM algorithms hosted in the LV flight computers, further reducing risk for the SLS launch infrastructure, the SLS LV, and most importantly the crew.
Advanced Control Considerations for Turbofan Engine Design
This paper covers the application of a model-based engine control (MBEC) methodology featuring a self tuning on-board model for an aircraft turbofan engine simulation. The nonlinear engine model is capable of modeling realistic engine performance, allowing for a verification of the advanced control methodology over a wide range of operating points and life cycle conditions. The on-board model is a piece-wise linear model derived from the nonlinear engine model and updated using an optimal tuner Kalman Filter estimation routine, which enables the on-board model to self-tune to account for engine performance variations. MBEC is used here to show how advanced control architectures can improve efficiency during the design phase of a turbofan engine by reducing conservative operability margins. The operability margins that can be reduced, such as stall margin, can expand the engine design space and offer potential for efficiency improvements. Application of MBEC architecture to a nonlinear engine simulation is shown to reduce the thrust specific fuel consumption by approximately 1% over the baseline design, while maintaining safe operation of the engine across the flight envelope.
Model Based Mission Assurance: Emerging Opportunities for Robotic Systems
The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).
Towards a Framework for Modeling Space Systems Architectures
Topics covered include: 1) Statement of the problem: a) Space system architecture is complex; b) Existing terrestrial approaches must be adapted for space; c) Need a common architecture methodology and information model; d) Need appropriate set of viewpoints. 2) Requirements on a space systems model. 3) Model Based Engineering and Design (MBED) project: a) Evaluated different methods; b) Adapted and utilized RASDS & RM-ODP; c) Identified useful set of viewpoints; d) Did actual model exchanges among selected subset of tools. 4) Lessons learned & future vision.
Relating MBSE to Spacecraft Development: A NASA Pathfinder
The NASA Engineering and Safety Center (NESC) has sponsored a Pathfinder Study to investigate how Model Based Systems Engineering (MBSE) and Model Based Engineering (MBE) techniques can be applied by NASA spacecraft development projects. The objectives of this Pathfinder Study included analyzing both the products of the modeling activity, as well as the process and tool chain through which the spacecraft design activities are executed. Several aspects of MBSE methodology and process were explored. Adoption and consistent use of the MBSE methodology within an existing development environment can be difficult. The Pathfinder Team evaluated the possibility that an "MBSE Template" could be developed as both a teaching tool as well as a baseline from which future NASA projects could leverage. Elements of this template include spacecraft system component libraries, data dictionaries and ontology specifications, as well as software services that do work on the models themselves. The Pathfinder Study also evaluated the tool chain aspects of development. Two chains were considered: 1. The Development tool chain, through which SysML model development was performed and controlled, and 2. The Analysis tool chain, through which both static and dynamic system analysis is performed. Of particular interest was the ability to exchange data between SysML and other engineering tools such as CAD and Dynamic Simulation tools. For this study, the team selected a Mars Lander vehicle as the element to be designed. The paper will discuss what system models were developed, how data was captured and exchanged, and what analyses were conducted.